mirror of
https://github.com/langgenius/dify.git
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Merge branch 'main' into feat/support-knowledge-metadata
# Conflicts: # api/core/rag/datasource/retrieval_service.py # api/core/workflow/nodes/code/code_node.py # api/services/dataset_service.py
This commit is contained in:
jyong
@ -1,9 +1,12 @@
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import concurrent.futures
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import json
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import threading
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from concurrent.futures import ThreadPoolExecutor
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from typing import Optional
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from flask import Flask, current_app
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from sqlalchemy.orm import load_only
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from configs import dify_config
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from core.rag.data_post_processor.data_post_processor import DataPostProcessor
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from core.rag.datasource.keyword.keyword_factory import Keyword
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from core.rag.datasource.vdb.vector_factory import Vector
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@ -27,6 +30,7 @@ default_retrieval_model = {
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class RetrievalService:
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# Cache precompiled regular expressions to avoid repeated compilation
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@classmethod
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def retrieve(
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cls,
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@ -42,77 +46,62 @@ class RetrievalService:
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):
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if not query:
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return []
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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if not dataset:
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return []
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dataset = cls._get_dataset(dataset_id)
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if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0:
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return []
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all_documents: list[Document] = []
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threads: list[threading.Thread] = []
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exceptions: list[str] = []
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# retrieval_model source with keyword
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if retrieval_method == "keyword_search":
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keyword_thread = threading.Thread(
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target=RetrievalService.keyword_search,
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kwargs={
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"flask_app": current_app._get_current_object(), # type: ignore
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"dataset_id": dataset_id,
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"query": query,
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"top_k": top_k,
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"all_documents": all_documents,
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"exceptions": exceptions,
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"document_ids_filter": document_ids_filter,
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},
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)
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threads.append(keyword_thread)
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keyword_thread.start()
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# retrieval_model source with semantic
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if RetrievalMethod.is_support_semantic_search(retrieval_method):
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embedding_thread = threading.Thread(
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target=RetrievalService.embedding_search,
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kwargs={
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"flask_app": current_app._get_current_object(), # type: ignore
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"dataset_id": dataset_id,
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"query": query,
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"top_k": top_k,
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"score_threshold": score_threshold,
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"reranking_model": reranking_model,
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"all_documents": all_documents,
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"retrieval_method": retrieval_method,
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"exceptions": exceptions,
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"document_ids_filter": document_ids_filter,
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},
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)
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threads.append(embedding_thread)
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embedding_thread.start()
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# retrieval source with full text
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if RetrievalMethod.is_support_fulltext_search(retrieval_method):
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full_text_index_thread = threading.Thread(
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target=RetrievalService.full_text_index_search,
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kwargs={
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"flask_app": current_app._get_current_object(), # type: ignore
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"dataset_id": dataset_id,
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"query": query,
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"retrieval_method": retrieval_method,
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"score_threshold": score_threshold,
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"top_k": top_k,
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"reranking_model": reranking_model,
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"all_documents": all_documents,
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"exceptions": exceptions,
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"document_ids_filter": document_ids_filter,
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},
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)
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threads.append(full_text_index_thread)
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full_text_index_thread.start()
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for thread in threads:
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thread.join()
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# Optimize multithreading with thread pools
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with ThreadPoolExecutor(max_workers=dify_config.RETRIEVAL_SERVICE_EXECUTORS) as executor: # type: ignore
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futures = []
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if retrieval_method == "keyword_search":
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futures.append(
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executor.submit(
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cls.keyword_search,
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flask_app=current_app._get_current_object(), # type: ignore
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dataset_id=dataset_id,
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query=query,
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top_k=top_k,
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all_documents=all_documents,
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exceptions=exceptions,
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)
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)
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if RetrievalMethod.is_support_semantic_search(retrieval_method):
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futures.append(
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executor.submit(
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cls.embedding_search,
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flask_app=current_app._get_current_object(), # type: ignore
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dataset_id=dataset_id,
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query=query,
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top_k=top_k,
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score_threshold=score_threshold,
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reranking_model=reranking_model,
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all_documents=all_documents,
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retrieval_method=retrieval_method,
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exceptions=exceptions,
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)
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)
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if RetrievalMethod.is_support_fulltext_search(retrieval_method):
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futures.append(
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executor.submit(
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cls.full_text_index_search,
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flask_app=current_app._get_current_object(), # type: ignore
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dataset_id=dataset_id,
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query=query,
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top_k=top_k,
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score_threshold=score_threshold,
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reranking_model=reranking_model,
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all_documents=all_documents,
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retrieval_method=retrieval_method,
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exceptions=exceptions,
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)
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)
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concurrent.futures.wait(futures, timeout=30, return_when=concurrent.futures.ALL_COMPLETED)
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if exceptions:
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exception_message = ";\n".join(exceptions)
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raise ValueError(exception_message)
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raise ValueError(";\n".join(exceptions))
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if retrieval_method == RetrievalMethod.HYBRID_SEARCH.value:
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data_post_processor = DataPostProcessor(
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@ -137,6 +126,10 @@ class RetrievalService:
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)
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return all_documents
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@classmethod
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def _get_dataset(cls, dataset_id: str) -> Optional[Dataset]:
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return db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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@classmethod
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def keyword_search(
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cls,
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@ -150,7 +143,7 @@ class RetrievalService:
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):
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with flask_app.app_context():
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try:
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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@ -179,12 +172,11 @@ class RetrievalService:
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):
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with flask_app.app_context():
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try:
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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vector = Vector(dataset=dataset)
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documents = vector.search_by_vector(
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query,
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search_type="similarity_score_threshold",
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@ -202,7 +194,7 @@ class RetrievalService:
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and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH.value
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):
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data_post_processor = DataPostProcessor(
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str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
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str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
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)
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all_documents.extend(
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data_post_processor.invoke(
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@ -232,13 +224,11 @@ class RetrievalService:
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):
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with flask_app.app_context():
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try:
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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vector_processor = Vector(
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dataset=dataset,
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)
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vector_processor = Vector(dataset=dataset)
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documents = vector_processor.search_by_full_text(cls.escape_query_for_search(query), top_k=top_k)
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if documents:
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@ -249,7 +239,7 @@ class RetrievalService:
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and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH.value
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):
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data_post_processor = DataPostProcessor(
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str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
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str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
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)
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all_documents.extend(
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data_post_processor.invoke(
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@ -268,64 +258,104 @@ class RetrievalService:
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def escape_query_for_search(query: str) -> str:
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return json.dumps(query).strip('"')
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@staticmethod
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def format_retrieval_documents(documents: list[Document]) -> list[RetrievalSegments]:
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records = []
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include_segment_ids = []
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segment_child_map = {}
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for document in documents:
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document_id = document.metadata.get("document_id")
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dataset_document = db.session.query(DatasetDocument).filter(DatasetDocument.id == document_id).first()
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if dataset_document:
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@classmethod
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def format_retrieval_documents(cls, documents: list[Document]) -> list[RetrievalSegments]:
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"""Format retrieval documents with optimized batch processing"""
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if not documents:
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return []
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try:
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# Collect document IDs
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document_ids = {doc.metadata.get("document_id") for doc in documents if "document_id" in doc.metadata}
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if not document_ids:
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return []
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# Batch query dataset documents
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dataset_documents = {
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doc.id: doc
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for doc in db.session.query(DatasetDocument)
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.filter(DatasetDocument.id.in_(document_ids))
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.options(load_only(DatasetDocument.id, DatasetDocument.doc_form, DatasetDocument.dataset_id))
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.all()
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}
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records = []
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include_segment_ids = set()
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segment_child_map = {}
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# Process documents
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for document in documents:
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document_id = document.metadata.get("document_id")
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if document_id not in dataset_documents:
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continue
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dataset_document = dataset_documents[document_id]
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if dataset_document.doc_form == IndexType.PARENT_CHILD_INDEX:
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# Handle parent-child documents
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child_index_node_id = document.metadata.get("doc_id")
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result = (
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db.session.query(ChildChunk, DocumentSegment)
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.join(DocumentSegment, ChildChunk.segment_id == DocumentSegment.id)
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child_chunk = (
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db.session.query(ChildChunk).filter(ChildChunk.index_node_id == child_index_node_id).first()
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)
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if not child_chunk:
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continue
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segment = (
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db.session.query(DocumentSegment)
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.filter(
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ChildChunk.index_node_id == child_index_node_id,
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DocumentSegment.dataset_id == dataset_document.dataset_id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == "completed",
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DocumentSegment.id == child_chunk.segment_id,
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)
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.options(
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load_only(
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DocumentSegment.id,
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DocumentSegment.content,
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DocumentSegment.answer,
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)
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)
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.first()
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)
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if result:
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child_chunk, segment = result
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if not segment:
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continue
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if segment.id not in include_segment_ids:
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include_segment_ids.append(segment.id)
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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map_detail = {
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"max_score": document.metadata.get("score", 0.0),
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"child_chunks": [child_chunk_detail],
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}
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segment_child_map[segment.id] = map_detail
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record = {
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"segment": segment,
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}
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records.append(record)
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else:
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
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segment_child_map[segment.id]["max_score"] = max(
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segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
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)
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else:
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if not segment:
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continue
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if segment.id not in include_segment_ids:
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include_segment_ids.add(segment.id)
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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map_detail = {
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"max_score": document.metadata.get("score", 0.0),
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"child_chunks": [child_chunk_detail],
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}
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segment_child_map[segment.id] = map_detail
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record = {
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"segment": segment,
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}
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records.append(record)
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else:
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
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segment_child_map[segment.id]["max_score"] = max(
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segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
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)
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else:
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index_node_id = document.metadata["doc_id"]
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# Handle normal documents
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index_node_id = document.metadata.get("doc_id")
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if not index_node_id:
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continue
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segment = (
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db.session.query(DocumentSegment)
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@ -340,16 +370,21 @@ class RetrievalService:
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if not segment:
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continue
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include_segment_ids.append(segment.id)
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include_segment_ids.add(segment.id)
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record = {
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"segment": segment,
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"score": document.metadata.get("score", None),
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"score": document.metadata.get("score"), # type: ignore
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}
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records.append(record)
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# Add child chunks information to records
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for record in records:
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if record["segment"].id in segment_child_map:
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record["child_chunks"] = segment_child_map[record["segment"].id].get("child_chunks", None)
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record["child_chunks"] = segment_child_map[record["segment"].id].get("child_chunks") # type: ignore
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record["score"] = segment_child_map[record["segment"].id]["max_score"]
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return [RetrievalSegments(**record) for record in records]
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return [RetrievalSegments(**record) for record in records]
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except Exception as e:
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db.session.rollback()
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raise e
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@ -119,8 +119,9 @@ class ChromaVector(BaseVector):
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for index in range(len(ids)):
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distance = distances[index]
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metadata = dict(metadatas[index])
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if distance >= score_threshold:
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metadata["score"] = distance
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score = 1 - distance
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if score > score_threshold:
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metadata["score"] = score
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doc = Document(
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page_content=documents[index],
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metadata=metadata,
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@ -179,7 +179,7 @@ class LindormVectorStore(BaseVector):
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if self._using_ugc:
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params["routing"] = self._routing
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response = self._client.search(index=self._collection_name, body=query, params=params)
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except Exception as e:
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except Exception:
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logger.exception(f"Error executing vector search, query: {query}")
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raise
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@ -355,7 +355,8 @@ def default_text_mapping(dimension: int, method_name: str, **kwargs: Any) -> dic
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}
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if excludes_from_source:
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mapping["mappings"]["_source"] = {"excludes": excludes_from_source} # e.g. {"excludes": ["vector_field"]}
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# e.g. {"excludes": ["vector_field"]}
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mapping["mappings"]["_source"] = {"excludes": excludes_from_source}
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if using_ugc and method_name == "ivfpq":
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mapping["settings"]["index"]["knn_routing"] = True
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